Within this research topic, we investigate the variability and the predictability of climate taking both internal variations and external forcing into account. Internal variations determine climate predictability on a wide range of scales: While slow variations constitute predictability, short-term unpredictable fluctuations can notably limit predictability. External forcing factors affect climate predictability through the different climate responses to these perturbations. Our research is primarily based on numerical climate simulations.
Work within A1 investigates the variability and predictability of climate from three perspectives:
- Climate predictability affected by and resulting from internal variability: we aim to identify the masking effects of unpredictable fluctuations and their role for large-scale dynamics and investigate the mechanisms and deterministic time scales of predictable slow climate component.
- Quantifying and reducing uncertainties relevant for predictability: we aim to develop a parametrization of ocean mixing that depends on the climate state.
- Predictability originating from responses to perturbations in external forcing: we analyse past millennium simulations and apply methods of non-equilibrium statistical mechanics to the climate system.
- Franzke, C. (2017). Extremes in dynamic-stochastic systems. Chaos, 27(1). doi:10.1063/1.4973541.
- Blender, R., Raible, C. C., & Franzke, C. L. E. (2016). Vorticity and geopotential height extreme values in ERA-Interim data during boreal winters. Quarterly Journal of the Royal Meteorological Society. doi:10.1002/qj.2944.
- Ludescher, J., Bunde, A., Franzke, C., & Schellnhuber, H. J. (2016). Long-term persistence enhances uncertainty about anthropogenic warming of West Antarctica. Climate Dynamics, 46(1), 263-271. doi:10.1007/s00382-015-2582-5.
- Ul Hasson, S., Pascale, S., Lucarini, V., & Böhner, J. (2016). Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections. Atmospheric Research, 180, 42-63. doi:10.1016/j.atmosres.2016.05.008.
- Graves, T., Gramacy, R. B., Franzke, C., & Watkins, N. W. (in press). Efficient Bayesian inference for long memory processes. Bayesian Analysis.